Abstract:
"Traffic Management System with vehicle detection and counting” is a research-based
initiative with the primary purpose of detecting, tracking, and classifying automobiles, but
it can also be applied to driver behavior detection, lane recognition, and other applications.
This framework can be used in a variety of domains, including public safety, accident
detection, vehicle detection, theft detection, parking lots, and human identification.
It can also be used to locate criminals on the road and traffic rule violators so that traffic
controllers can take swift action. People are expanding in number, and vehicles are
increasing in number as well. Due to a growth in the number of automobiles, highways and
roadways are becoming overcrowded. As a result, the frequency of accidents and violations
of traffic laws has skyrocketed. For traffic managers, vehicle detection and counting
become essential. As a result, we suggested a traffic management system framework.
Our work is mostly based on a video-based technique for vehicle recognition and counting
that employs the Python programming language OpenCV.
Visual Studio Code was used to create and implement the framework for this article. To
achieve real-time automatic vehicle detection and counting, software was combined with
Intel's OpenCV video stream processing system.
This framework can quickly recognize and track automobiles, as well as assist in the
counting of objects.
